{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1dcf73b2048>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建x轴和y轴\n",
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "y = np.array([3,5,7,6,2,6,10,15])\n",
    "# 绘制折线图 四个参数：x轴，y轴，颜色，线条宽度。r代表红色，g代表绿色\n",
    "# plt.plot(x,y,'r')\n",
    "plt.plot(x,y,'g',lw=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制柱状图 x轴 y轴 每个柱状图的宽度比例 透明度 颜色\n",
    "plt.bar(x,y,0.5,alpha=1,color='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'g',lw=5)\n",
    "plt.bar(x,y,0.5,alpha=1,color='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
